Local spectral anisotropy is a valid cue for figure–ground organization in natural scenes

Ramenahalli, Sudarshan; Mihalaş, Ştefan; Niebur, Ernst

doi:10.1016/j.visres.2014.08.012

Cited by 14 publications

(21 citation statements)

References 36 publications

(58 reference statements)

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“…While the contours included in natural and filled patches are the same, the other content features, including texture, color, and shading, could improve neuronal FG discrimination. Ramenahalli et al [14] reported that local spectral anisotropy between the figure and ground sides along a contour was capable of yielding a rate of correct FG assignment of almost 70%, using SVM. This result suggests that texture and shading include a fair amount of information that can be used for FG discrimination, although it is currently unclear how much information is used by V4 neurons.…”

Section: Plos Onementioning

confidence: 99%

“…In all these pioneering studies, the responses of single cells to well-controlled stimuli, such as textured squares, luminance-defined rectangles, and parameterized curvatures, were examined with the careful selection of cells based on their stimulus selectivity (e.g., orientation, color, and shape) and the spatial relations of their classical receptive fields (CRFs) with respect to the stimulus. [10] Recent studies have investigated FG organization in natural scenes, reporting crucial factors for FG based on local image features including contour shapes [11][12][13] and spectral anisotropy [14]. V2 neurons were shown to be selective to border ownership in natural images [15].…”

Section: Introductionmentioning

confidence: 99%

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Population coding of figure and ground in natural image patches by V4 neurons

et al. 2020

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Segmentation of a natural scene into objects and background is a fundamental but challenging task for recognizing objects. Investigating intermediate-level visual cortical areas with a focus on local information is a crucial step towards understanding the formation of the cortical representations of figure and ground. We examined the activity of a population of macaque V4 neurons during the presentation of natural image patches and their respective variations. The natural image patches were optimized to exclude the influence of global context but included various characteristics of local stimulus. Around one fourth of the patchresponsive V4 neurons exhibited significant modulation of firing activity that was dependent on the positional relation between the figural region of the stimulus and the classical receptive field of the neuron. However, the individual neurons showed low consistency in figureground modulation across a variety of image patches (55-62%), indicating that individual neurons were capable of correctly signaling figure and ground only for a limited number of stimuli. We examined whether integration of the activity of multiple neurons enabled higher consistency across a variety of natural patches by training a support vector machine to classify figure and ground of the stimuli from the population firing activity. The integration of the activity of a few tens of neurons yielded discrimination accuracy much greater than that of single neurons (up to 85%), suggesting a crucial role of population coding for figure-ground discrimination in natural images.

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Population coding of figure and ground in natural image patches by V4 neurons

et al. 2020

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“…The EE on the outer side of the inner edge of a torus (or donut), for example, is easily perceived as the closer figure, even though it is highly concave and fully surrounds the visible region of the surface visible through the central hole. In complex natural scenes, figure-ground organization is processed from a combination of information about global features (e.g., region convexity) and local features (e.g., T-junctions) (Ramenahalli, Mihalas, & Niebur, 2011, 2014. Because EEs can be extracted from local spatial filters around the edge, they are more efficient for figure-ground computations than global properties.…”

mentioning

confidence: 99%

Gradient cuts and extremal edges in relative depth and figure–ground perception

Ghose

Palmer

2015

Atten Percept Psychophys

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Extremal edges (EEs) are borders consisting of luminance gradients along the projected edge of a partly self-occluding curved surface (e.g., a cylinder), with equiluminant contours (ELCs) that run approximately parallel to that edge. Gradient cuts (GCs) are similar luminance gradients with ELCs that intersect (are Bcut^by) an edge that could be due to occlusion. EEs are strongly biased toward being seen as closer/figural surfaces (Palmer & Ghose, Psychological Science, 19(1), [77][78][79][80][81][82][83] 2008). Do GCs produce a complementary bias toward being seen as ground? Experiment 1 shows that, with EEs on the opposite side, GCs produce a ground bias that increases with increasing ELC angles between ELCs and the shared edge. Experiment 2 shows that, with flat surfaces on the opposite side, GCs do not produce a ground bias, suggesting that more than one factor may be operating. We suggest that two partially dissociable factors may operate for curved surfaces-ELC angle and 3-D surface convexity-that reinforce each other in the figural cues of EEs but compete with each other in GCs. Moreover, this figural bias is modulated by the presence of EEs and GCs, as specified by the ELC angle between ELCs and the shared contour.

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“…In fact, Geisler and Perry ( 2009 ) observed that edges with an inconsistent polarity are less likely to belong to the same contour. Recently, it has been reported that even low-level cues, such as the sharpness of an edge or local anisotropies in spectral power can be informative about figure-ground organization (Ramenahalli et al, 2014 ; Vilankar et al, 2014 ).…”

Section: Intermediate Computationsmentioning

confidence: 99%

A conceptual framework of computations in mid-level vision

Kubilius

Wagemans

Beeck

2014

Front. Comput. Neurosci.

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If a picture is worth a thousand words, as an English idiom goes, what should those words—or, rather, descriptors—capture? What format of image representation would be sufficiently rich if we were to reconstruct the essence of images from their descriptors? In this paper, we set out to develop a conceptual framework that would be: (i) biologically plausible in order to provide a better mechanistic understanding of our visual system; (ii) sufficiently robust to apply in practice on realistic images; and (iii) able to tap into underlying structure of our visual world. We bring forward three key ideas. First, we argue that surface-based representations are constructed based on feature inference from the input in the intermediate processing layers of the visual system. Such representations are computed in a largely pre-semantic (prior to categorization) and pre-attentive manner using multiple cues (orientation, color, polarity, variation in orientation, and so on), and explicitly retain configural relations between features. The constructed surfaces may be partially overlapping to compensate for occlusions and are ordered in depth (figure-ground organization). Second, we propose that such intermediate representations could be formed by a hierarchical computation of similarity between features in local image patches and pooling of highly-similar units, and reestimated via recurrent loops according to the task demands. Finally, we suggest to use datasets composed of realistically rendered artificial objects and surfaces in order to better understand a model's behavior and its limitations.

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Local spectral anisotropy is a valid cue for figure–ground organization in natural scenes

Cited by 14 publications

References 36 publications

Population coding of figure and ground in natural image patches by V4 neurons

Population coding of figure and ground in natural image patches by V4 neurons

Gradient cuts and extremal edges in relative depth and figure–ground perception

A conceptual framework of computations in mid-level vision

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